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Learn more about the technology and data we use to provide state-of-the-art fraud and compliance risk prevention.

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AI-Powered Deposit Analytics

Swift’s deposit analytics engine is powered by a combination of advanced machine learning models and real-time data processing infrastructure. By leveraging AWS Glue for ETL operations and Apache Kafka for real-time streaming, we process millions of transactions per second to identify patterns and trends. Our analytics pipeline integrates with:
  • Transaction data streams
  • Account metadata
  • Historical deposit patterns
  • Market indicators
  • Customer chat logs
  • Customer support tickets
The processed data is then analyzed by our ML models, which are fine-tuned using Claude 3.5 LLMs to extract meaningful insights. These models help financial institutions:
  • Predict deposit volatility
  • Identify seasonal patterns
  • Detect unusual withdrawal behavior
  • Forecast liquidity needs
The insights are delivered through both our REST API and real-time webhooks, allowing seamless integration with your existing systems. For implementation details, see our API Reference.

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Device Intelligence & Behavior Biometrics Risk SDK

Our advanced Risk SDK detects high-risk devices and behavioral anomalies. Utilizing the collected data, we can accurately identify and flag suspicious devices and sessions involved in creating synthetic accounts, account takeovers, and funding accounts with stolen payment methods. The Risk SDK is compatible with both web and native applications.

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Machine Learning

At the core of Swift’s fraud prevention capabilities lies our innovative approach to machine learning model development. We’ve pioneered a collaborative network that pools anonymized fraud data across financial institutions, enabling us to build highly sophisticated supervised learning models. This shared intelligence means that when fraudsters target one institution, our models are already prepared to protect others from similar attacks. Beyond supervised learning, our platform employs cutting-edge unsupervised learning algorithms that excel at identifying emerging fraud patterns and anomalous behaviors before they become widespread threats.

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No-Code or Low-Code Rule Development

Swift’s no-to-low-code Rule Editor empowers you to create and customize fraud prevention rules without writing any code. Our platform comes with hundreds of pre-built rules that protect your business from day one, leveraging both standard and custom data points. The real-time rule development environment allows you to respond rapidly to emerging threats and stop fraudulent activities as they occur. Before deploying new rules, you can evaluate their effectiveness using our shadow-mode feature. This allows you to test and refine rules by monitoring their performance against real traffic without impacting your production environment. Once you’re confident in a rule’s accuracy, you can seamlessly transition it into active enforcement.

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Integrated Dashboard

Our integrated dashboard provides a unified view for visualizing and investigating potential threats while monitoring known bad actors. The customer-centric interface gives you complete access to:
  • Device and session data analytics
  • Checkpoint and rule configurations
  • Real-time rule editing capabilities
  • Comprehensive reporting tools
  • Anomaly detection alerts
  • Block/allow list management
  • Alert review and remediation queues
  • Full user management and access controls
This centralized platform serves as a complete command center for your risk and compliance operations, eliminating the need to switch between multiple tools and interfaces.